About
I'm Hana, and welcome to my personal website! I'm a CS PhD student broadly interested in different aspects of systems & networking, machine learning, and algorithms. My recent focus is on data-driven wireless communications and networking. More specifically, my research aims to improve IEEE 802.11 WLANs design in the MAC layer. For more information on my previous and ongoing projects please visit Projects page. Currently, I'm working with Dr. Tamer Nadeem at Mobile Systems & Intelligent Communication (MuSIC) Lab. Prior to my PhD program, I worked as research assistant in LIG lab under the supervision of Prof. Franck Rousseau and Columbia University under the supervision of Prof. Gil Zussman.
Welcome to my personal website. I am Hannah, a Postdoctoral Scholar at the EECS Department of UC Berkeley, working in the NetSys Lab. My research interests span various aspects of system and network performance, privacy, security, and Internet of Things (IoT) protocols. Currently, my research focuses on the privacy and security dimensions of 5G networks. Previously, I have explored data-driven approaches to wireless communications and networking. My doctoral dissertation specifically concentrated on designing Media Access Control (MAC) layer protocols for WLANs, aligned with the recent IEEE 802.11 standards (11ac/ax, and upcoming be). For an in-depth view of my past and present projects, please refer to the Projects page. In addition to my role at UC Berkeley, I hold positions as a Visiting Research Scientist at the LIG Lab, supervised by Prof. Franck Rousseau; Research Associate at Inria Lyon; and Visiting Professor at North Carolina A&T. I am also collaborating with CISPA research institute (Germany), working alongside Dr. Singh. In the past, I worked as a Senior Wireless Engineer at Qualcomm, a Research Intern at Skylark Wireless, and a Research Assistant at Columbia University, under the guidance of Prof. Gil Zussman, and several more.
machine learning & algorithms.
- Birthday: 1 May 1995
- Website: www.example.com
- Phone: +123 456 7890
- City: New York, USA
- Age: 30
- Degree: Master
- PhEmailone: Hannah@gmail.com
- Freelance: Available
Fun fact: I have an identical twin! Her name is Haniyeh and she does research in (human) languages.
Research
Far more than 5G
Faros® is a true Massive MIMO wireless solution that scales to any size deployment on any sub-6 GHz spectrum.
Skills
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News
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December, 2020
- Selected as one of the finalists for the 2021 NCWIT Collegiate Award.
- Serving as TPC for SmartEdge 2021 Workshop.
September, 2020
- I will be serving on a shadow PC for the EuroSys 2021.
March, 2020
- Our paper "CONVINCE: Collaborative Video Analytics at the edge" was nominated for best WiP paper award in IEEE PerCom'20 Conference.
February, 2020
- I have received USENIX student travel grant to attend and present my work "Machine-guided network protocols" at USENIX nsdi'20 Conference.
- I have presented our paper "MAC Protocol Design Optimization Using Deep Learning" in IEEE ICAIIC'20 Conference in Fukuoka, Japan.
January, 2020
- I am nominated by the CS department chair to attend VCU strategic planning annual meeting.
December, 2019
- I received VCU School of Engineering graduate student award.
- I have received NSF student travel grant to attend ACM CoNEXT'19.
- I am visiting Facebook, Google, and YouTube campuses.
November, 2019
- I am invited among the 12 students to give a talk about my research on video analytics at the edge at AT&T Annual Graduate Student Symposium, New York, NY.
- I'm presenting my paper at The ACM SenSys'19 AIChallengeIoT Workshop.
- I received the best poster award at The ACM SenSys'19 N^2Women Workshop for my work on "MAC Layer Optimization Using Deep Learning".
- I received ACM SIGMOBILE, N^2Women, VCU school of Engineering, and VCU Graduate grants for my work on "Collaborative Video Analytics".
- You can read VCU school of Engineering coverage on my work on "Collaborative Video Analytics".
October, 2019
- I received Full Grace Hopper Celebration Scholarship to attend GHC'19.
- I have started my collaboration with my mentor at Google Brain (the collaboration is acknowleged here).
- I received NSF student travel grant to attend MobiHoc'19 in Catania, Italy.
August
July
June, 2019
- I received NSF travel grant to attend INFOCOM'19 in Paris, France.
- I'm honored to receive full scholarship to attend CRA-W grad cohort workshop in Chicago, IL.
- I have passed my PhD qualifying exam!
- I have received NSF travel grant to attend and present my work at IEEE PerCom'19 in Kyoto, Japan.
- I'm honored to receive N^2Women Young Researcher Fellowship to organize N^2Women meeting in IEEE PerCom'19 Conference in Kyoto, Japan.
- I'm visiting Systems and Algorithms Laboratory (SysAL), Imperial College London, London, UK.
March
February, 2019
- I recevived NSF student travel grant to present my work at HotMobile'19 in Santa Cruz, CA.
- I presented our paper "Challenges and Limitationsin Designing MAC Protocols Using Reinforcement Learning" in ICAIIC'19 Conference in Okinawa, Japan.
December, 2018
- I have received NSF travel grant to attend and present my work at ACM CoNEXT'18 in Crete, Greece.
- I'm visiting Drakkar team, LIG lab, Grenoble, France
February, 2018
- I'm attending ICIN Conference in Paris, France.
- I'm visiting Drakkar team, LIG lab, Grenoble, France.
Awards
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- All
- 2020
- 2019
- 2018
Projects
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LATTE: VR-Aware MU-MIMO Optimization
VR-Aware MU-MIMO Optimization
In this project, we propose a cross-layer optimization framework to enable mu-VR over 802.11ac/ax, a multi-user VR system for untethered mobile devices over 802.11ac/ax Wi-Fi. Our cross-layer design introduces novel optimizations in both application and wireless lower layers. Multi-user MIMO (MU-MIMO) is a technique in 802.11ac and 802.11ax that improves spectral efficiency by allowing concurrent communication between one AP and multiple clients. In practice, the expected gain is not always achieved and is sometimes even negative. 802.11ac/ax joint MU-MIMO user grouping and scheduling is crucial for multi-user applications. Although Mu-MIMO is introduced in 802.11ac and ax to improve spectral efficiency by allowing concurrent communication, it may introduce high delays and low throughput if AP selects the wrong users to group in a MU-MIMO transmission, as users with correlated channels cause high packet losses due to interference. Therefore, using a commodity 802.11ac AP, we first experimentally show that factors such as user mobility or user device type are important to both MU-MIMO MAC and application layer optimizations. Based on this observation, the AP can have a predictive approach to decide whether a user can benefit from participating in MU-MIMO rather than current reactive algorithms. We present our design and its evaluation on COTS smartphones and laptops over 802.11ac Wi-Fi.
DeepMAC: Data-Driven MAC Protocol Design Optimization
Data-Driven MAC Protocol Design Optimization
Networking protocols are designed through long-time and hard-work human efforts. Machine Learning (ML)-based solutions have been developed for communication protocol design to avoid manual efforts to tune individual protocol parameters. While other proposed ML-based methods mainly focus on tuning individual protocol parameters (e.g., adjusting contention window), our main contribution is to propose a novel Deep Reinforcement Learning (DRL)-based framework to systematically design and evaluate networking protocols. We decouple a protocol into a set of parametric modules, each representing a main protocol functionality that is used as DRL input to better understand the generated protocols design optimization and analyze them in a systematic fashion. As a case study, we introduce and evaluate DeepMAC a framework in which a MAC protocol is decoupled into a set of blocks across popular flavors of 802.11 WLANs (e.g., 802.11 a/b/g/n/ac). We are interested to see what blocks are selected by DeepMAC across different networking scenarios and whether DeepMAC is able to adapt to network dynamics.
AMuSe: Online Video Rate Adaptation Using WiFi Multicast
Online Video Rate Adaptation
Video delivery over wireless networks is challenging due to the lack of spectrum and reliability issues. This challenge is exacerbated in dense venues. To address this issue, Wi-Fi multicast with the ability to simultaneously multicast the same video contents to a group of users, has gained attention. For successful video delivery, the content providers are interested in evaluating the performance of such traffic from the final users' perspective, that is, their Quality of Experience (QoE). The QoE ties together user perception, experience, and expectations to application and network performance. However, ensuring high QoE for multicast video streaming is challenging. Although, there have been considerable efforts in the literature to design Adaptive Bitrate (ABR) streaming algorithms to ensure the video QoE, applying these approaches to wireless multicast is not straightforward due to lack of feedback and unreliable transmissions. To overcome these issues, transmission and video rate can be jointly controlled to ensure the video QoE using Wi-Fi multicast. In this project, we have collaborated with the Adaptive Multicast Services (AMuSe) project at wim.net Lab at Columbia University which is an end to end system for high quality video delivery to a large number of users in dense environments which leverages Wi-Fi multicast. The project involves improvements of the DYnamic Video and Rate (DYVR) algorithm which is an online control algorithm for jointly multicast transmission and video rates adaptation. We present a new channel estimation method for this algorithm. We evaluate the algorithm using the new channel estimation through extensive experiments in a push-based platform consisting of Android devices and an off-the-shelf wireless Access Point (AP). We show that our new channel estimation method improves the total performance of the algorithm significantly. We also compare two distinct versions of this algorithm against current ABR based state-of-the-art approaches.
CONVINCE: Collaborative Video Analytics at the Edge
Collaborative Video Analytics at the Edge
Today, video cameras are deployed in dense for monitoring physical places e.g., city, industrial, or agricultural sites. In the current systems, each camera node sends its feed to a cloud server individually. However, this approach suffers from several hurdles including higher computation cost, large bandwidth requirement for analyzing the enormous data, and privacy concerns. In dense deployment, video nodes typically demonstrate a significant spatio-temporalcorrelation. To overcome these obstacles in current approaches, this project introduces CONVINCE, a new approach to look at the network cameras as a collective entity that enables collaborative video analytics pipeline among cameras. CONVINCE aims at 1) reducing the computation cost and bandwidth requirements by leveraging spatio-temporal correlations among cameras in eliminating redundant frames intelligently, and ii) improving vision algorithms’ accuracy by enabling collaborative knowledge sharing among relevant cameras. Our results demonstrate that CONVINCE achieves an object identification accuracy of ∼91%, by transmitting only about ∼25% of all the recorded frames.
Awards
Publications
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Towards A Learning-Based Framework for Self-Driving Design of Networking Protocols
(Available upon request.)
MAC Protocol Design Optimization Using Deep Learning
In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan.
Unboxing MAC Protocol Design Optimization Using Deep Learning
In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Austin, TX, USA.
Challenges and Limitations in Automating the Design of MAC Protocols Using Machine-Learning
In 2019 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, Okinawa, Japan.
CONVINCE: Collaborative Cross-Camera Video Analytics at the Edge
In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Austin, TX, USA.
Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges
In SenSys'19: The 17th ACM Conference on Embedded Networked Sensor Systems AIChallengeIoT Workshop 2019, New York, NY, USA.